FROM CLAUSES TO COGNITION: A COMPUTATIONAL ANALYSIS OF LINGUISTIC COMPLEXITY IN INTERMEDIATE BOOK 2

Abstract

Abstract

This study investigates the syntactic complexity of Intermediate English Book 2 within the Pakistani curriculum through a corpus-based and computational approach. Drawing on methods from Natural Language Processing, the analysis employs established syntactic indices—mean length of sentence (MLS), mean length of clause (MLC), clauses per sentence (C/S), and dependent clauses per T-unit (DC/TU)—to examine structural patterns across lessons and text types. The findings reveal that the textbook exhibits moderate to high linguistic complexity, with MLS values ranging approximately between 17 and 20 and consistent subordination levels (DC/TU ≈ 0.4), aligning with B2–C1 proficiency benchmarks. The results further demonstrate that complexity is not uniform but varies across genres: scientific and historical texts show higher syntactic density and subordination, while narrative and humorous texts rely relatively more on coordination and linear structures. Clause-level analysis indicates a strong presence of dependent clauses, particularly adverbial and relative clauses, which function to encode causal relationships, temporal sequencing, and descriptive detail. From a cognitive perspective, these features contribute to increased processing demands, requiring learners to engage with hierarchically structured information. The study argues that syntactic complexity in Book 2 functions as a cognitively demanding yet pedagogically purposeful feature, supporting the transition from intermediate to advanced proficiency. By integrating NLP-based analysis with SLA theory, the research provides an empirical framework for evaluating textbook difficulty and highlights the need for scaffolded instruction to manage cognitive load. The findings have implications for learners, educators, and curriculum planners, emphasizing the importance of balancing linguistic richness with accessibility in instructional materials.